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Rubinstein-Taybi syndrome (RSTS) is a rare autosomal dominant genetic disorder characterized by mental retardation, broad thumbs, and hooked nose and so on. RSTS can cause multiple organ hypoplasia and multiple system hypoplasia (such as endocrine system, digestive system, urinary system, etc.) , and even involve the skin (such as pilomatrixomas, hirsutism, etc.) . Moreover, it has been recently reported to be associated with increased risk of cancer. However, relevant research about RSTS is still very limited. By use of clinical characteristics and genetic testing, only some cases can be diagnosed, and the diagnosis for many other cases additionally requires an in-depth analysis of etiology and pathogenesis of RSTS. This paper reports a suspected RSTS patient treated in the Affiliated Hospital of Guilin Medical University. Whole exome sequencing showed that the patient has a mutation in the CREBBP gene: c.3832G>A (p.Glu1278Lys) , but her parents do not have, indicating that it is a new mutation. By the analysis of the genetic testing result with clinical characteristics, the patient was diagnosed with RSTS. It is very difficult to make a diagnosis of RSTS. By providing a detailed description of clinical features of RSTS, this report is conducive to the study of genotype-phenotype correlation of RSTS, and to clinical diagnosis and treatment of RSTS.

Human brain function parcellation is an important way to reveal the separation of brain functions. However, most of the existing parcellation methods can not deal with the high dimension and low signal-to-noise ratio of functional magnetic resonance imaging (fMRI) data, so they show the problem of weaker search ability and poorer parcellation results. To alleviate this problem, a human brain functional parcellation method based on artificial jellyfish search optimization (AJSO) algorithm is proposed. Firstly, a functional correlation matrix is calculated based on the preprocessed fMRI data and mapped to a low-dimensional space. Then, a food is encoded as a cluster solution composed of multiple functional cluster centers and the improved AJSO is used to search for better food. The time control mechanism integrated with iterative stagnation is used to control the artificial jellyfish to perform active or passive motion, so as to improve the global search ability. Step size determination strategy guided by fitness is designed for active movement to enhance scientific and targeted search of artificial jellyfish. Finally, according to the principle of minimum distance, the cluster label of each row data in the correlation matrix is obtained and mapped to the corresponding voxels. Experiments on real fMRI data show that compared with other partitioning methods, the new method not only has higher searching ability, but also can obtain better spatial structures and stronger functional consistency. In this study, artificial jellyfish search optimization algorithm is applied to brain functional parcellation, which provides a more effective method of brain functional parcellation.